Abstract The ability of accurate localize and characterize cells in light sheet fluorescence microscopy (LSFM) image is indispensable for shedding new light on the understanding of three dimensional structures of the whole brain. In our previous work, we have successfully developed a 2D nuclear segmentation method for the nuclear cleared microscopy images using deep learning techniques. Although the convolutional neural networks show promise in segmenting cells in LSFM images, our previous work is confined in 2D segmentation scenario and suffers from the limited number of annotated data. In this project, we aim to develop a high throughput 3D cell segmentation engine, with the focus on improving the segmentation accuracy and generality. First, we will develop a cloud based semi-automatic annotation platform using the strength of virtual reality (VR) and crowd sourcing. The user-friendly annotation environment and stereoscopic view in VR can significantly improve the efficiency of manual annotation. We design a semi-automatic annotation workflow to largely reduce human intervention, and thus improve both the accuracy and the replicability of annotation across different users. Enlightened by the spirit of citizen science, we will extend the annotation software into a crowd sourcing platform which allows us to obtain a massive number of manual annotations in short time. Second, we will develop a fully 3D cell segmentation engine using 3D convolutional neural networks trained with the 3D annotated samples. Since it is often difficult to acquire isotropic LSFM images, we will further develop a super resolution method to impute a high resolution image to facilitate the 3D cell segmentation. Third, we will develop a transfer learning framework to make our 3D cell segmentation engine general enough to the application of novel LSFM data which might have significant gap of image appearance due to different imaging setup or clearing/staining protocol. This general framework will allow us to rapidly develop a specific cell segmentation solution for new LSFM data with very few or even no manual annotations, by transferring the existing 3D segmentation engine that has been trained with a sufficient number of annotated samples. Fourth, we will apply our computational tools to several pilot neuroscience studies: (1) Investigating how topoisomerase I (one of the autism linked transcriptional regulators) regulates brain structure, and (2) Investigating genetic influence on cell types in the developing human brain by quantifying the number of progenitor cells in fetal cortical tissue. Successful carrying out our project will have wide-reaching impact in neuroscience community in visualizing and analyzing complete cellular resolution maps of individual cell types within healthy and disease brain. The improved cell segmentation engine in 3D allows scientists from all over the world to share and process each other?s data accurately and efficiently, thus increasing reproducibility and power.